The long-term objective of this research is to improve the clinical impact of computer-aided diagnosis systems. Specifically in this project, a calibrated data set will be developed that will be used to optimize a computer- aided detection (CADe) algorithm. Currently CADe algorithms are optimized for detecting cancers in images (so-called stand-alone performance, which is measured without considering the radiologist user). Our proposed method will maximize radiologists' performance in reading screening mammograms (i.e., CADe will be optimized for clinical benefit to the radiologist, not stand-alone performance) Two hypotheses will be tested: Hypothesis 1: Radiologists using a CADe scheme optimized using the calibrated dataset will have higher performance than when using a CADe scheme optimized using current methods; and Hypothesis 2: The improved performance of radiologists using an arbitrary CADe scheme, as measured in an observer study, can be predicted with sufficient accuracy using a calibrated dataset. This will be accomplished through the following specific aims: 1. Develop the calibrated database based on a group of radiologists reading without CADe and a group of radiologists analyzing individual CADe marks; 2. Validate the calibrated dataset through an observer study; and 3. Develop a novel method for optimizing CADe to maximize radiologists' performance. The calibrated dataset should improve the clinical effectiveness of CADe. Current clinical studies show that by using CADe, radiologists can increase their sensitivity for cancer detection by 10%. However, radiologists ignore up to 70% of correct CADe marked cancers. We believe that by optimizing CADe systems to maximize the benefit to the radiologist, as oppose to maximizing CADe performance without considering the effect on radiologists, will lead to larger gains in sensitivity by radiologists.